
import sys
sys.path.append('..')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import datetime

# Use seaborn style defaults and set default figure size
plt.style.use('seaborn-pastel')
sns.set_theme(style="whitegrid", color_codes=True)


from functions import rplot, qqplot


dfmod = pd.read_csv('data/aqi_data_mod.csv')
dfobs = pd.read_csv('data/aqi_data_obs.csv')


dfmod['datetime'] = pd.to_datetime(dfmod.date) + pd.to_timedelta(dfmod.hour)
dfobs['datetime'] = pd.to_datetime(dfobs.date) + pd.to_timedelta(dfobs.hour)


dfmod = dfmod.drop(['date', 'hour'], axis=1)
dfobs = dfobs.drop(['date', 'hour'], axis=1)


dfmod = dfmod.set_index(dfmod.datetime)
dfobs = dfobs.set_index(dfobs.datetime)


dfobs.head()


# Do interpolatoin of the observed data

dfobs = dfobs.interpolate(method = 'time')


dfobs.isna().sum()

obs_PM2.5          0
obs_PM2.5_stdev    0
obs_PM10           0
obs_PM10_stdev     0
datetime           0
dtype: int64


df = pd.concat([dfmod, dfobs], axis=1)


dfnov = df.loc["2020-11-01":"2020-11-30"]
dfdec = df.loc["2020-12-01":"2020-12-31"]
dfjan = df.loc["2021-01-01":"2021-01-31"]


dfnov


# Q-Q plot for PM2.5 concentration

fig = plt.figure(figsize=(20,10))

plt.subplot(141)
qqplot.plot(df['obs_PM2.5'], df['mod_PM2.5'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
plt.ylabel('Model PM2.5', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(142)
qqplot.plot(dfnov['obs_PM2.5'], dfnov['mod_PM2.5'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(143)
qqplot.plot(dfdec['obs_PM2.5'], dfdec['mod_PM2.5'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(144)
qqplot.plot(dfjan['obs_PM2.5'], dfjan['mod_PM2.5'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Q-Q Plot for PM2.5 concentration (in $\mu g/m^3$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/qqplot.plot25.png')


# Q-Q plot for PM10 concentration

fig = plt.figure(figsize=(20,10))

plt.subplot(141)
qqplot.plot(df['obs_PM10'], df['mod_PM10'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
plt.ylabel('Model PM2.5', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(142)
qqplot.plot(dfnov['obs_PM10'], dfnov['mod_PM10'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(143)
qqplot.plot(dfdec['obs_PM10'], dfdec['mod_PM10'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(144)
qqplot.plot(dfjan['obs_PM10'], dfjan['mod_PM10'], c='g', alpha=0.4, edgecolor='k', rug=False)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Q-Q Plot for PM10 concentration (in $\mu g/m^3$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/qqplot.plot10.png')


# Scatter-Correlation plot for PM2.5 concentration

fig = plt.figure(figsize=(20,20))

plt.subplot(221)
rplot.plot(df, df['obs_PM2.5'], df['mod_PM2.5'], color='k', alpha=0.7)
# plt.xlabel('Observed PM2.5', fontsize=16)
plt.ylabel('Model PM2.5', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(222)
rplot.plot(dfnov, dfnov['obs_PM2.5'], dfnov['mod_PM2.5'], color='k', alpha=0.7)
# plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(223)
rplot.plot(dfdec, dfdec['obs_PM2.5'], dfdec['mod_PM2.5'], color='k', alpha=0.7)
plt.xlabel('Observed PM2.5', fontsize=16)
plt.ylabel('Model PM2.5', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(224)
rplot.plot(dfdec, dfjan['obs_PM2.5'], dfjan['mod_PM2.5'], color='k', alpha=0.7)
plt.xlabel('Observed PM2.5', fontsize=16)
# plt.ylabel('Model PM2.5', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Scatter Plots for PM2.5 concentration (in $\mu g/m^3$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/rplot.plot25.png')


# Scatter-Correlation plot for PM10 concentration

fig = plt.figure(figsize=(20,20))

plt.subplot(221)
rplot.plot(df, df['obs_PM10'], df['mod_PM10'], color='k', alpha=0.7)
# plt.xlabel('Observed PM10', fontsize=16)
plt.ylabel('Model PM10', fontsize=16)
plt.title('NDJ Combined',fontsize=18)

plt.subplot(222)
rplot.plot(dfnov, dfnov['obs_PM10'], dfnov['mod_PM10'], color='k', alpha=0.7)
# plt.xlabel('Observed PM10', fontsize=16)
plt.ylabel('Model PM10', fontsize=16)
plt.title('November 2020', fontsize=18)

plt.subplot(223)
rplot.plot(dfdec, dfdec['obs_PM10'], dfdec['mod_PM10'], color='k', alpha=0.7)
plt.xlabel('Observed PM10', fontsize=16)
plt.ylabel('Model PM10', fontsize=16)
plt.title('December 2020', fontsize=18)

plt.subplot(224)
rplot.plot(dfdec, dfjan['obs_PM10'], dfjan['mod_PM10'], color='k', alpha=0.7)
plt.xlabel('Observed PM10', fontsize=16)
plt.ylabel('Model PM10', fontsize=16)
plt.title('January 2021', fontsize=18)

fig.suptitle('Scatter Plots for PM10 concentration (in $\mu g/m^3$) in Delhi', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/rplot.plot10.png')


metnov = pd.read_csv('./data/met_data_nov_utc.csv')
metdec = pd.read_csv('./data/met_data_dec_utc.csv')
metjan = pd.read_csv('./data/met_data_jan_utc.csv')


metnov['datetime'] = pd.to_datetime(metnov.date) + pd.to_timedelta(metnov.hour)
metdec['datetime'] = pd.to_datetime(metdec.time)
metjan['datetime'] = pd.to_datetime(metjan.datetime)


metnov = metnov.set_index(metnov.datetime)
metdec = metdec.set_index(metdec.datetime)
metjan = metjan.set_index(metjan.datetime)

metnov.drop(['date', 'hour', 'datetime'], axis=1, inplace=True)
metdec.drop(['time', 'datetime'], axis=1, inplace=True)
metjan.drop(['datetime'], axis=1, inplace=True)


metjan.head()


# Add the missing values of datetime in November and December as NaN

idx_nov = pd.date_range('2020-11-01 00:00:00','2020-11-30 23:00:00', freq='H')
idx_dec = pd.date_range('2020-12-01 00:00:00','2020-12-31 23:00:00', freq='H')

metnov = metnov.reindex(idx_nov, fill_value=np.nan)
metdec = metdec.reindex(idx_dec, fill_value=np.nan)


met = pd.concat([metnov, metdec, metjan], axis=0)


met_aqi = pd.concat([df,met], axis=1)
met_aqi_nov = pd.concat([dfnov, metnov], axis=1)
met_aqi_dec = pd.concat([dfdec, metdec], axis=1)
met_aqi_jan = pd.concat([dfjan, metjan], axis=1)


met_aqi.drop(['datetime'], inplace=True, axis=1)
met_aqi_nov.drop(['datetime'], inplace=True, axis=1)
met_aqi_dec.drop(['datetime'], inplace=True, axis=1)
met_aqi_jan.drop(['datetime'], inplace=True, axis=1)


## Making new dataframes for each Met parameter with the pm2.5 and pm10 data


temp_aqi = met_aqi[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_temp', 'obs_temp']]
temp_aqi_nov = met_aqi_nov[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_temp', 'obs_temp']]
temp_aqi_dec = met_aqi_dec[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_temp', 'obs_temp']]
temp_aqi_jan = met_aqi_jan[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_temp', 'obs_temp']]

swdown_aqi = met_aqi[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_swdown', 'obs_swupper']]
swdown_aqi_nov = met_aqi_nov[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_swdown', 'obs_swupper']]
swdown_aqi_dec = met_aqi_dec[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_swdown', 'obs_swupper']]
swdown_aqi_jan = met_aqi_jan[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_swdown', 'obs_swupper']]

wd_aqi = met_aqi[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_wd', 'obs_wd']]
wd_aqi_nov = met_aqi_nov[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_wd', 'obs_wd']]
wd_aqi_dec = met_aqi_dec[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_wd', 'obs_wd']]
wd_aqi_jan = met_aqi_jan[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_wd', 'obs_wd']]

ws_aqi = met_aqi[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_ws', 'obs_ws']]
ws_aqi_nov = met_aqi_nov[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_ws', 'obs_ws']]
ws_aqi_dec = met_aqi_dec[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_ws', 'obs_ws']]
ws_aqi_jan = met_aqi_jan[['mod_PM2.5', 'mod_PM10', 'obs_PM2.5', 'obs_PM10', 'mod_ws', 'obs_ws']]


wd_aqi.head()


#### Temperature vs PM2.5

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(temp_aqi, temp_aqi.dropna()['mod_temp'], temp_aqi.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(temp_aqi, temp_aqi.dropna()['obs_temp'], temp_aqi.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(temp_aqi_nov, temp_aqi_nov.dropna()['mod_temp'], temp_aqi_nov.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(temp_aqi_nov, temp_aqi_nov.dropna()['obs_temp'], temp_aqi_nov.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(temp_aqi_dec, temp_aqi_dec.dropna()['mod_temp'], temp_aqi_dec.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(temp_aqi_dec, temp_aqi_dec.dropna()['obs_temp'], temp_aqi_dec.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(temp_aqi_jan, temp_aqi_jan.dropna()['mod_temp'], temp_aqi_jan.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(temp_aqi_jan, temp_aqi_jan.dropna()['obs_temp'], temp_aqi_jan.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Temperature and PM2.5 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/temp25.png')


#### Temperature vs PM10

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(temp_aqi, temp_aqi.dropna()['mod_temp'], temp_aqi.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(temp_aqi, temp_aqi.dropna()['obs_temp'], temp_aqi.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(temp_aqi_nov, temp_aqi_nov.dropna()['mod_temp'], temp_aqi_nov.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(temp_aqi_nov, temp_aqi_nov.dropna()['obs_temp'], temp_aqi_nov.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(temp_aqi_dec, temp_aqi_dec.dropna()['mod_temp'], temp_aqi_dec.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(temp_aqi_dec, temp_aqi_dec.dropna()['obs_temp'], temp_aqi_dec.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(temp_aqi_jan, temp_aqi_jan.dropna()['mod_temp'], temp_aqi_jan.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(temp_aqi_jan, temp_aqi_jan.dropna()['obs_temp'], temp_aqi_jan.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Temperature ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Temperature and PM10 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/temp10.png')


#### Downwelling SW radiation vs PM2.5

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(swdown_aqi, swdown_aqi.dropna()['mod_swdown'], swdown_aqi.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(swdown_aqi, swdown_aqi.dropna()['obs_swupper'], swdown_aqi.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(swdown_aqi_nov, swdown_aqi_nov.dropna()['mod_swdown'], swdown_aqi_nov.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(swdown_aqi_nov, swdown_aqi_nov.dropna()['obs_swupper'], swdown_aqi_nov.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(swdown_aqi_dec, swdown_aqi_dec.dropna()['mod_swdown'], swdown_aqi_dec.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(swdown_aqi_dec, swdown_aqi_dec.dropna()['obs_swupper'], swdown_aqi_dec.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(swdown_aqi_jan, swdown_aqi_jan.dropna()['mod_swdown'], swdown_aqi_jan.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($W/m^2$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(swdown_aqi_jan, swdown_aqi_jan.dropna()['obs_swupper'], swdown_aqi_jan.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($W/m^2$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Downwelling SW radiation and PM2.5 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/swdown25.png')


#### Downwelling SW radiation vs PM10

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(swdown_aqi, swdown_aqi.dropna()['mod_swdown'], swdown_aqi.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(swdown_aqi, swdown_aqi.dropna()['obs_swupper'], swdown_aqi.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(swdown_aqi_nov, swdown_aqi_nov.dropna()['mod_swdown'], swdown_aqi_nov.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(swdown_aqi_nov, swdown_aqi_nov.dropna()['obs_swupper'], swdown_aqi_nov.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(swdown_aqi_dec, swdown_aqi_dec.dropna()['mod_swdown'], swdown_aqi_dec.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(swdown_aqi_dec, swdown_aqi_dec.dropna()['obs_swupper'], swdown_aqi_dec.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(swdown_aqi_jan, swdown_aqi_jan.dropna()['mod_swdown'], swdown_aqi_jan.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($W/m^2$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(swdown_aqi_jan, swdown_aqi_jan.dropna()['obs_swupper'], swdown_aqi_jan.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($W/m^2$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Downwelling SW radiation and PM10 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/swdown10.png')


#### WD vs PM2.5

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(wd_aqi, wd_aqi.dropna()['mod_wd'], wd_aqi.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(wd_aqi, wd_aqi.dropna()['obs_wd'], wd_aqi.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(wd_aqi_nov, wd_aqi_nov.dropna()['mod_wd'], wd_aqi_nov.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(wd_aqi_nov, wd_aqi_nov.dropna()['obs_wd'], wd_aqi_nov.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(wd_aqi_dec, wd_aqi_dec.dropna()['mod_wd'], wd_aqi_dec.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(wd_aqi_dec, wd_aqi_dec.dropna()['obs_wd'], wd_aqi_dec.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(wd_aqi_jan, wd_aqi_jan.dropna()['mod_wd'], wd_aqi_jan.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Downwelling SW radiation (in deg.)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(wd_aqi_jan, wd_aqi_jan.dropna()['obs_wd'], wd_aqi_jan.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Downwelling SW radiation (in deg.)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Wind Direction and PM2.5 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/wd25.png')


#### WD vs PM10

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(wd_aqi, wd_aqi.dropna()['mod_wd'], wd_aqi.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(wd_aqi, wd_aqi.dropna()['obs_wd'], wd_aqi.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(wd_aqi_nov, wd_aqi_nov.dropna()['mod_wd'], wd_aqi_nov.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(wd_aqi_nov, wd_aqi_nov.dropna()['obs_wd'], wd_aqi_nov.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(wd_aqi_dec, wd_aqi_dec.dropna()['mod_wd'], wd_aqi_dec.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(wd_aqi_dec, wd_aqi_dec.dropna()['obs_wd'], wd_aqi_dec.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(wd_aqi_jan, wd_aqi_jan.dropna()['mod_wd'], wd_aqi_jan.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Downwelling SW radiation (in deg.)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(wd_aqi_jan, wd_aqi_jan.dropna()['obs_wd'], wd_aqi_jan.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Downwelling SW radiation (in deg.)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Wind Direction and PM10 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/wd10.png')


#### ws vs PM2.5

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(ws_aqi, ws_aqi.dropna()['mod_ws'], ws_aqi.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(ws_aqi, ws_aqi.dropna()['obs_ws'], ws_aqi.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(ws_aqi_nov, ws_aqi_nov.dropna()['mod_ws'], ws_aqi_nov.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(ws_aqi_nov, ws_aqi_nov.dropna()['obs_ws'], ws_aqi_nov.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(ws_aqi_dec, ws_aqi_dec.dropna()['mod_ws'], ws_aqi_dec.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(ws_aqi_dec, ws_aqi_dec.dropna()['obs_ws'], ws_aqi_dec.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(ws_aqi_jan, ws_aqi_jan.dropna()['mod_ws'], ws_aqi_jan.dropna()['mod_PM2.5'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($m/s$)", fontsize=12)
plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(ws_aqi_jan, ws_aqi_jan.dropna()['obs_ws'], ws_aqi_jan.dropna()['obs_PM2.5'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($m/s$)", fontsize=12)
# plt.ylabel("PM2.5 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Wind Speed and PM2.5 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/ws25.png')


#### ws vs PM10

fig = plt.figure(figsize=(15,22))

# NDJ Combined
plt.subplot(421)
rplot.plot(ws_aqi, ws_aqi.dropna()['mod_ws'], ws_aqi.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Model)", fontsize=16)

plt.subplot(422)
rplot.plot(ws_aqi, ws_aqi.dropna()['obs_ws'], ws_aqi.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("NDJ Combined (Observation)", fontsize=16)

# November
plt.subplot(423)
rplot.plot(ws_aqi_nov, ws_aqi_nov.dropna()['mod_ws'], ws_aqi_nov.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Model)", fontsize=16)

plt.subplot(424)
rplot.plot(ws_aqi_nov, ws_aqi_nov.dropna()['obs_ws'], ws_aqi_nov.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("November (Observation)", fontsize=16)

# December
plt.subplot(425)
rplot.plot(ws_aqi_dec, ws_aqi_dec.dropna()['mod_ws'], ws_aqi_dec.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Model)", fontsize=16)

plt.subplot(426)
rplot.plot(ws_aqi_dec, ws_aqi_dec.dropna()['obs_ws'], ws_aqi_dec.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
# plt.xlabel("Downwelling SW radiation ($^{o}C$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("December (Observation)", fontsize=16)

# January
plt.subplot(427)
rplot.plot(ws_aqi_jan, ws_aqi_jan.dropna()['mod_ws'], ws_aqi_jan.dropna()['mod_PM10'], type='compare', color='darkred', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($m/s$)", fontsize=12)
plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Model)", fontsize=16)

plt.subplot(428)
rplot.plot(ws_aqi_jan, ws_aqi_jan.dropna()['obs_ws'], ws_aqi_jan.dropna()['obs_PM10'], type='compare', color='darkblue', alpha=0.6)
plt.xlabel("Downwelling SW radiation ($m/s$)", fontsize=12)
# plt.ylabel("PM10 Concentration ($\mu g/m^3$)", fontsize=12)
plt.title("January (Observation)", fontsize=16)

fig.suptitle('Scatter Plots for Wind Speed and PM10 Concentration', fontsize=27, y=0.99)
fig.tight_layout()
plt.savefig('./images/met_aqi_compare/ws10.png')

	obs_PM2.5	obs_PM2.5_stdev	obs_PM10	obs_PM10_stdev	datetime
datetime
2020-11-01 00:00:00	387.90	158.73	561.44	165.77	2020-11-01 00:00:00
2020-11-01 01:00:00	348.11	143.39	486.04	159.66	2020-11-01 01:00:00
2020-11-01 02:00:00	326.03	120.39	516.67	134.42	2020-11-01 02:00:00
2020-11-01 03:00:00	302.90	111.29	514.41	125.31	2020-11-01 03:00:00
2020-11-01 04:00:00	309.05	104.38	520.55	102.16	2020-11-01 04:00:00

	mod_PM2.5	mod_PM2.5_stdev	mod_PM10	mod_PM10_stdev	datetime	obs_PM2.5	obs_PM2.5_stdev	obs_PM10	obs_PM10_stdev	datetime
datetime
2020-11-01 00:00:00	152.61	51.64	241.71	105.98	2020-11-01 00:00:00	387.90	158.73	561.44	165.77	2020-11-01 00:00:00
2020-11-01 01:00:00	159.82	53.31	254.49	111.38	2020-11-01 01:00:00	348.11	143.39	486.04	159.66	2020-11-01 01:00:00
2020-11-01 02:00:00	160.04	51.57	252.47	109.05	2020-11-01 02:00:00	326.03	120.39	516.67	134.42	2020-11-01 02:00:00
2020-11-01 03:00:00	156.73	40.47	242.03	87.21	2020-11-01 03:00:00	302.90	111.29	514.41	125.31	2020-11-01 03:00:00
2020-11-01 04:00:00	156.32	41.95	232.03	84.02	2020-11-01 04:00:00	309.05	104.38	520.55	102.16	2020-11-01 04:00:00
...	...	...	...	...	...	...	...	...	...	...
2020-11-30 19:00:00	264.13	127.55	476.49	252.76	2020-11-30 19:00:00	264.66	85.95	434.93	126.75	2020-11-30 19:00:00
2020-11-30 20:00:00	236.13	102.84	421.26	207.41	2020-11-30 20:00:00	257.07	80.09	426.47	128.26	2020-11-30 20:00:00
2020-11-30 21:00:00	225.05	94.71	395.51	188.52	2020-11-30 21:00:00	237.17	79.33	391.33	123.03	2020-11-30 21:00:00
2020-11-30 22:00:00	219.43	96.73	381.02	186.26	2020-11-30 22:00:00	230.55	79.61	366.59	110.47	2020-11-30 22:00:00
2020-11-30 23:00:00	212.33	97.10	364.74	186.20	2020-11-30 23:00:00	217.03	71.24	346.66	102.93	2020-11-30 23:00:00

	mod_temp	obs_temp	mod_wd	obs_wd	mod_ws	obs_ws	mod_swdown	obs_swlower	obs_swupper	obs_lwupper	obs_lwlower
datetime
2021-01-01 00:00:00	NaN	6.682222	NaN	79.756946	NaN	0.900347	NaN	3.510417	-0.786550	618.516667	620.381667
2021-01-01 01:00:00	NaN	6.623194	NaN	107.330557	NaN	1.106306	NaN	4.742067	15.913250	615.495000	617.031667
2021-01-01 02:00:00	NaN	7.471944	NaN	112.248613	NaN	0.464417	NaN	12.202367	106.011333	587.153333	589.123333
2021-01-01 03:00:00	NaN	10.370833	NaN	104.988888	NaN	0.823708	NaN	23.618167	257.441667	524.011667	544.338333
2021-01-01 04:00:00	NaN	13.849861	NaN	138.116665	NaN	1.378944	NaN	37.442667	379.663333	458.161667	504.655000

	mod_PM2.5	mod_PM10	obs_PM2.5	obs_PM10	mod_wd	obs_wd
2020-11-01 00:00:00	152.61	241.71	387.90	561.44	NaN	NaN
2020-11-01 01:00:00	159.82	254.49	348.11	486.04	NaN	NaN
2020-11-01 02:00:00	160.04	252.47	326.03	516.67	NaN	NaN
2020-11-01 03:00:00	156.73	242.03	302.90	514.41	NaN	NaN
2020-11-01 04:00:00	156.32	232.03	309.05	520.55	NaN	NaN

SRFP AQI data extra plots for Delhi city¶

Q-Q plot¶

Scatter - Correlation Plot¶

Correlation plots of AQI with Meteorological data¶

Plots¶

Temperature¶

SWDOWN¶

WD¶

WS¶